Overview

We used the PLIER R package with the collection of 12999 gene sets as a prior information matrix priorMat available in the package comprising canonical, immune and chemgen pathways from MSigDB (same with the eLife paper)

We used 26 immune and blood cell traits in this part, they are: “mch”,“mchc”,“mcv”,“rdw”,“ret”,“baso”,“plt”,“pct”,“pdw”,“mpv”,“hct”,“hgb”,“ret”,“mono”,“T1D”, “EUR.IBD”,“EUR.UC”,“EUR.CD”,“ukb.allasthma”,“eo”,“wbc”,“rbc”,“myeloid_wbc”,“gran”,“lymph”,“neut”,“allergy”

We got 98 LVs.

Enrichment summary – computed from ACAT p-values for all pairs

We computed both ACAT p-values and K-S p-values. The p-values seem better (qqplot below). So the enrichments were computed using p-values.

The enrichment at p-value < 0.05 level is : **129/(2548*0.05) ==1.012559**

The enrichment at p-value < 0.01 level is : **31/(2548*0.01) ==1.216641**

The enrichment at p-value < 0.01 level is : **7/(2548*0.001) ==2.747253**

K-S and ACAT summary

Enrichment results

We have 2548 trait-factor pairs in total. 31 pairs have more than 2 SNPs with FDR <0.2.

There are 4 pairs show sign consistency (have p-values < 0.05 in resampling, 2.58 fold enrichment). There are 1 pairs at p-value < 0.01, 3.23 fold enrichment).

Reverse causality checking

For the pairs have p-value < 0.05 in resampling, we checked the effect size correlation could be due to reverse causality.

We first used all SNPs associated with traits(pval<5E-8). We computed the MBE estimator and the p-values of the estimator. When computing the IVW, we exchanged the beta.outcome and beta.exposure.

Then we removed the SNPs have FDR < 0.2, just used the remaining SNP to compute IVW and do the fitting.

**The suffix "_reverse" are the results for traits -> LV (using all candidate SNPs after LD Clumping), the suffix "_rremove" are the results for traits -> LV (using all candidate SNPs after LD Clumping but remove the SNPs have fdr < 0.2)**

SNP details

Effectsize plots

Colocalization results

For the SNPs above, we did colocalization analysis.

The method for colocalization analysis were described in 3.3

+-250kb from the target locus

Two plots for the locus has the highest PP4:

ret_lv73

ret_lv73_rs2523990

ret_lv73_rs10067881

EUR.UC_lv46

EUR.UC_lv46_rs1359946

EUR.UC_lv46_rs254559

rbc_lv32

rbc_lv32_rs17699658

rbc_lv32_rs13103322

ukb.allasthma_lv35

ukb.allasthma_lv35_rs61816761

ukb.allasthma_lv35_rs35621564

+-500kb from the target locus

ret_lv73

Plots for the locus has the highest PP4:

ret_lv73_rs2523990

ret_lv73_rs10067881

EUR.UC_lv46

EUR.UC_lv46_rs1359946

EUR.UC_lv46_rs254559

rbc_lv32

rbc_lv32_rs17699658

rbc_lv32_rs13103322

ukb.allasthma_lv35

LV35 has three related pathways reported by PLIER, including CHEMELLO_SOLEUS_VS_EDL_MYOFIBERS_UP, RICKMAN_HEAD_AND_NECK_CANCER_F, and CHEN_LVAD_SUPPORT_OF_FAILING_HEART_UP.

ukb.allasthma_lv35_rs61816761

ukb.allasthma_lv35_rs35621564

ORA Enrichment results

The method for ORA were described in 3.4

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sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5        rstudioapi_0.11   whisker_0.3-2     knitr_1.30       
 [5] magrittr_1.5      R6_2.4.1          rlang_0.4.8       highr_0.8        
 [9] stringr_1.4.0     tools_3.6.1       DT_0.15           xfun_0.18        
[13] git2r_0.26.1      crosstalk_1.1.0.1 htmltools_0.5.0   ellipsis_0.3.1   
[17] rprojroot_1.3-2   yaml_2.2.1        digest_0.6.25     tibble_3.0.3     
[21] lifecycle_0.2.0   crayon_1.3.4      later_1.1.0.1     htmlwidgets_1.5.2
[25] vctrs_0.3.4       promises_1.1.1    fs_1.5.0          glue_1.4.2       
[29] evaluate_0.14     rmarkdown_1.13    stringi_1.5.3     compiler_3.6.1   
[33] pillar_1.4.6      backports_1.1.10  jsonlite_1.7.1    httpuv_1.5.1     
[37] pkgconfig_2.0.3